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        <h1 id="ML-DL"><a href="#ML-DL" class="headerlink" title="ML/DL"></a>ML/DL</h1><h2 id="过拟合、欠拟合"><a href="#过拟合、欠拟合" class="headerlink" title="过拟合、欠拟合"></a>过拟合、欠拟合</h2><ul>
<li>理解</li>
<li>过拟合<ul>
<li>增加样本（增加的样本分布要与原始样本的分布尽可能不同）</li>
<li>减少特征数量</li>
<li>正则化，例如l2正则化后，虽然参数数量没有变化，但是参数趋近于0，则特征的重要性减小了</li>
<li>样本采样</li>
<li>特征采样</li>
</ul>
</li>
</ul>
<h2 id="分类问题中正负例"><a href="#分类问题中正负例" class="headerlink" title="分类问题中正负例"></a>分类问题中正负例</h2><ul>
<li>过采样</li>
<li>降采样</li>
<li>AUC ROC</li>
<li>调整阈值</li>
</ul>
<h2 id="激活函数"><a href="#激活函数" class="headerlink" title="激活函数"></a>激活函数</h2><p>作用：本来是wx+b是线性的，加入非线性函数，使得理论上可以逼近所有函数。</p>
<ul>
<li>sigmoid f(x) = 1/(1+exp(-x))<ul>
<li>幂运算，计算量大</li>
<li>梯度消失与梯度爆炸：BP时每层都要求激活函数的导，这个导数如果小于1经过多次之后就会趋近于0，梯度消失；如果大于1，多次之后会非常大，梯度爆炸</li>
<li>其输出是非零均值的，例如某个神经元经过sigmoid之后的输出都大于0，此时输入到下一层后，因为wx+b，所以对w的导数为x，即导数大于0，导致的结果就是BP时w都正方向更新</li>
</ul>
</li>
<li>tanh f(x) = (1-exp(-2x)) / (1+exp(-2x))<ul>
<li>幂运算，计算量大</li>
<li>解决了非零均值的问题</li>
<li>梯度消失与梯度爆炸仍然存在</li>
</ul>
</li>
<li>ReLU f(x) = max(0,x)<ul>
<li>速度快（输出速度，收敛速度）</li>
<li>正区间解决了梯度消失问题</li>
<li>非零均值</li>
<li>Dead ReLU：某些神经元永远都不会被激活</li>
</ul>
</li>
<li>Leaky ReLU<ul>
<li>解决了Dead ReLU问题 </li>
</ul>
</li>
</ul>
<h2 id="Logistic-Regression"><a href="#Logistic-Regression" class="headerlink" title="Logistic Regression"></a>Logistic Regression</h2><ul>
<li>如何优化的？loss f 策略</li>
<li>如何避免过拟合<ul>
<li>增加样本（增加的样本分布要与原始样本的分布尽可能不同）</li>
<li>减少特征数量</li>
<li>正则化，例如l2正则化后，虽然参数数量没有变化，但是参数趋近于0，则特征的重要性减小了</li>
<li>每次迭代，调整学习速率</li>
</ul>
</li>
</ul>
<h2 id="线性模型解决非线性问题"><a href="#线性模型解决非线性问题" class="headerlink" title="线性模型解决非线性问题"></a>线性模型解决非线性问题</h2><ul>
<li>例如LR，SVM都是线性模型，但是使用非线性的核函数可以实现对非线性问题的解决</li>
</ul>
<h1 id="语言"><a href="#语言" class="headerlink" title="语言"></a>语言</h1><h2 id="python"><a href="#python" class="headerlink" title="python"></a>python</h2><ul>
<li>python数据类型：Numbers（数字）、String（字符串）、List（列表）、Tuple（元组）、Dictionary（字典）</li>
<li>元组和数组的差别：<ul>
<li>元组()不能修改，数组[]可以修改</li>
<li>字典{}，集合set()是无序的</li>
</ul>
</li>
</ul>
<h1 id="DS"><a href="#DS" class="headerlink" title="DS"></a>DS</h1><ul>
<li>二叉树的遍历</li>
<li><p>很大的一个文件寻找频率TOP-k的词</p>
</li>
<li><p>一个非递减序列，寻找某个数最后出现的位置</p>
</li>
<li><p>归并排序</p>
  <figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br></pre></td><td class="code"><pre><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">merge_sort</span><span class="params">(num)</span>:</span></span><br><span class="line">    <span class="keyword">if</span> len(num) &lt;= <span class="number">1</span>:</span><br><span class="line">        <span class="keyword">return</span> num</span><br><span class="line">    mid = num // <span class="number">2</span></span><br><span class="line">    left = merge_sort(num[:mid]) <span class="comment"># 从下往上的递归中，每次递归得到的left和right是排好序的，需要对两者合并后做排序</span></span><br><span class="line">    right = merge_sort(num[mid:])</span><br><span class="line">    <span class="keyword">return</span> merge_result(left, right)</span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">merge_result</span><span class="params">(left, right)</span>:</span></span><br><span class="line">    i,j = <span class="number">0</span>,<span class="number">0</span></span><br><span class="line">    result = []</span><br><span class="line">    <span class="keyword">while</span> i &lt; len(left) <span class="keyword">and</span> j &lt; len(right):</span><br><span class="line">        <span class="keyword">if</span> left[i] &lt;= right[j]:</span><br><span class="line">            result.append(left[i])</span><br><span class="line">            i += <span class="number">1</span></span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            result.append(right[j])</span><br><span class="line">            j += <span class="number">1</span></span><br><span class="line">    <span class="keyword">return</span> result+left[i:]+right[j:]</span><br></pre></td></tr></table></figure>
</li>
<li><p>快排</p>
  <figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br></pre></td><td class="code"><pre><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">quick_sort1</span><span class="params">(num,left,right)</span>:</span></span><br><span class="line">    <span class="keyword">if</span> left &gt;= right:</span><br><span class="line">        <span class="keyword">return</span></span><br><span class="line">    low = left</span><br><span class="line">    high = right</span><br><span class="line">    pivot = num[left]</span><br><span class="line">    <span class="keyword">while</span> left &lt; right:</span><br><span class="line">        <span class="keyword">while</span> left &lt; right <span class="keyword">and</span> num[right] &gt; pivot:</span><br><span class="line">            right -= <span class="number">1</span></span><br><span class="line">        num[left] = num[right]</span><br><span class="line">        <span class="keyword">while</span> left &lt; right <span class="keyword">and</span> num[left] &lt;= pivot:</span><br><span class="line">            left += <span class="number">1</span></span><br><span class="line">        num[right] = num[left]</span><br><span class="line">    num[right] = pivot</span><br><span class="line">    quick_sort1(num, low, left<span class="number">-1</span>)</span><br><span class="line">    quick_sort1(num, right+<span class="number">1</span>, high)</span><br></pre></td></tr></table></figure>
  <figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br></pre></td><td class="code"><pre><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">quick_sort2</span><span class="params">(num,left,right)</span>:</span></span><br><span class="line">    <span class="keyword">while</span> left &lt; right:</span><br><span class="line">        p = partition(num, left, right)</span><br><span class="line">        quick_sort2(num, left, p<span class="number">-1</span>)</span><br><span class="line">        quick_sort2(num, p+<span class="number">1</span>, right)</span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">partition</span><span class="params">(num, left, right)</span>:</span></span><br><span class="line">    pivot = num[right] <span class="comment"># 先挪左指针，用右边界作为pivot</span></span><br><span class="line">    i = left</span><br><span class="line">    <span class="keyword">for</span> j <span class="keyword">in</span> range(left,right):</span><br><span class="line">        <span class="keyword">if</span> num[j] &lt;= pivot:</span><br><span class="line">            i += <span class="number">1</span></span><br><span class="line">            num[i], num[j] = num[j], num[i]</span><br><span class="line">    num[i+<span class="number">1</span>], num[right] = num[right], num[i+<span class="number">1</span>] <span class="comment"># 把基准数移过来</span></span><br><span class="line">    <span class="keyword">return</span> i+<span class="number">1</span></span><br></pre></td></tr></table></figure>
  <figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">quick_sort3</span><span class="params">(num)</span>:</span></span><br><span class="line">    <span class="keyword">if</span> len(num) &lt;= <span class="number">1</span>:</span><br><span class="line">    <span class="keyword">return</span> num</span><br><span class="line">    pivot = num[<span class="number">0</span>]</span><br><span class="line">    left = [num[i] <span class="keyword">for</span> i <span class="keyword">in</span> range(len(num)) <span class="keyword">if</span> num[i] &lt; pivot]</span><br><span class="line">    right = [num[i] <span class="keyword">for</span> i <span class="keyword">in</span> range(len(num)) <span class="keyword">if</span> num[i] &gt; pivot]</span><br><span class="line">    <span class="keyword">return</span> quick_sort3(left) + pivot + quick_sort3(right)</span><br></pre></td></tr></table></figure>
</li>
<li><p>O(N)复杂度在一个数组中寻找两数和为指定数的下标</p>
</li>
</ul>
<h1 id="逻辑"><a href="#逻辑" class="headerlink" title="逻辑"></a>逻辑</h1><ul>
<li>一个筛子产生0~9的随机数，要求概率相等</li>
<li>理发师数量估计</li>
</ul>
<h1 id="操作系统-计网"><a href="#操作系统-计网" class="headerlink" title="操作系统 + 计网"></a>操作系统 + 计网</h1><ul>
<li><p>进程和线程</p>
<p>进程和线程的主要差别在于它们是不同的操作系统资源管理方式。进程有独立的地址空间，一个进程崩溃后，在保护模式下不会对其它进程产生影响，而线程只是一个进程中的不同执行路径。线程有自己的堆栈和局部变量，但线程之间没有单独的地址空间，一个线程死掉就等于整个进程死掉，所以多进程的程序要比多线程的程序健壮，但在进程切换时，耗费资源较大，效率要差一些。但对于一些要求同时进行并且又要共享某些变量的并发操作，只能用线程，不能用进程。<br>1) 简而言之,一个程序至少有一个进程,一个进程至少有一个线程。<br>2) 线程的划分尺度小于进程，使得多线程程序的并发性高。<br>3) 进程在执行过程中拥有独立的内存单元，而多个线程共享内存，从而极大地提高了程序的运行效率。<br>4) 线程在执行过程中与进程还是有区别的。每个独立的线程有一个程序运行的入口、顺序执行序列和程序的出口。但是线程不能够独立执行，必须依存在应用程序中，由应用程序提供多个线程执行控制。<br>5) 从逻辑角度来看，多线程的意义在于一个应用程序中，有多个执行部分可以同时执行。但操作系统并没有将多个线程看做多个独立的应用，来实现进程的调度和管理以及资源分配。这就是进程和线程的重要区别。</p>
</li>
<li>同步和异步<ul>
<li>消息的通知机制</li>
<li>涉及到IO通知机制；所谓同步，就是发起调用后，被调用者处理消息，必须等处理完才直接返回结果，没处理完之前是不返回的，调用者主动等待结果；所谓异步，就是发起调用后，被调用者直接返回，但是并没有返回结果，等处理完消息后，通过状态、通知或者回调函数来通知调用者，调用者被动接收结果。</li>
</ul>
</li>
<li>阻塞和非阻塞<ul>
<li>程序等待调用结果时的状态</li>
<li>涉及到CPU线程调度；所谓阻塞，就是调用结果返回之前，该执行线程会被挂起，不释放CPU执行权，线程不能做其它事情，只能等待，只有等到调用结果返回了，才能接着往下执行；所谓非阻塞，就是在没有获取调用结果时，不是一直等待，线程可以往下执行，如果是同步的，通过轮询的方式检查有没有调用结果返回，如果是异步的，会通知回调。</li>
</ul>
</li>
<li>TCP和UDP<ul>
<li>基于连接（TCP）与无连接（UDP）； </li>
<li>对系统资源的要求（TCP较多，UDP少）； </li>
<li>UDP程序结构较简单； </li>
<li>流模式与数据报模式 ；</li>
<li>TCP保证数据正确性，UDP可能丢包，TCP保证数据顺序，UDP不保证。</li>
</ul>
</li>
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              <div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-1"><a class="nav-link" href="#ML-DL"><span class="nav-number">1.</span> <span class="nav-text">ML/DL</span></a><ol class="nav-child"><li class="nav-item nav-level-2"><a class="nav-link" href="#过拟合、欠拟合"><span class="nav-number">1.1.</span> <span class="nav-text">过拟合、欠拟合</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#分类问题中正负例"><span class="nav-number">1.2.</span> <span class="nav-text">分类问题中正负例</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#激活函数"><span class="nav-number">1.3.</span> <span class="nav-text">激活函数</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#Logistic-Regression"><span class="nav-number">1.4.</span> <span class="nav-text">Logistic Regression</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#线性模型解决非线性问题"><span class="nav-number">1.5.</span> <span class="nav-text">线性模型解决非线性问题</span></a></li></ol></li><li class="nav-item nav-level-1"><a class="nav-link" href="#语言"><span class="nav-number">2.</span> <span class="nav-text">语言</span></a><ol class="nav-child"><li class="nav-item nav-level-2"><a class="nav-link" href="#python"><span class="nav-number">2.1.</span> <span class="nav-text">python</span></a></li></ol></li><li class="nav-item nav-level-1"><a class="nav-link" href="#DS"><span class="nav-number">3.</span> <span class="nav-text">DS</span></a></li><li class="nav-item nav-level-1"><a class="nav-link" href="#逻辑"><span class="nav-number">4.</span> <span class="nav-text">逻辑</span></a></li><li class="nav-item nav-level-1"><a class="nav-link" href="#操作系统-计网"><span class="nav-number">5.</span> <span class="nav-text">操作系统 + 计网</span></a></li></ol></div>
            

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